The min()
method returns the smallest element of an array along an axis.
Example
import numpy as np
array1 = np.array([10, 12, 14, 11, 5])
# return the smallest element
minValue= np.min(array1)
print(minValue)
# Output: 5
min() Syntax
The syntax of min()
is:
numpy.min(array, axis = None, out = None, keepdims = <no value>, initial=<no value>, where=<no value>)
min() Arguments
The min()
method takes six arguments:
array
- input arrayaxis
(optional) - axis along which minimum value is returned (int
)out
(optional) - array to store the outputkeepdims
(optional) - whether to preserve the input array's dimension (bool
)initial
(optional) - the minimum value of an output element (scalar)where
(optional) - elements to include in the minimum value calculation(array
ofbool
)
min() Return Value
The min()
method returns the smallest element.
Note: If at least one element of the input array is NaN
, min()
will return NaN
.
Example 1: min() With 2D Array
The axis
argument defines how we can handle the smallest element in a 2D array.
- If
axis
=None
, the array is flattened and the minimum of the flattened array is returned. - If
axis
= 0, the smallest element in each column is returned. - If
axis
= 1, the smallest element in each row is returned.
import numpy as np
array = np.array([[10, 17, 25],
[15, 11, 22]])
# return the smallest element of the flattened array
minValue = np.min(array)
print('The smallest element in the flattened array: ', minValue)
# return the smallest element in each column
minValue = np.min(array, axis = 0)
print('The smallest element in each column (axis 0): ', minValue)
# return the smallest element in each row
minValue = np.min(array, axis = 1)
print('The smallest element in each row (axis 1): ', minValue)
Output
The smallest element in the flattened array: 10 The smallest element in each column (axis 0): [10 11 22] The smallest element in each row (axis 1): [10 11]
Example 2: Use out to Store the Result in Desired Location
In our previous examples, the min()
function generated a new output array.
However, we can use an existing array to store the output using the out
argument.
import numpy as np
array1 = np.array([[10, 17, 25],
[15, 11, 22],
[11, 19, 20]])
# create an empty array
array2= np.array([0, 0, 0])
# pass the 'out' argument to store the result in array2
np.min(array1, axis = 0, out = array2)
print(array2)
Output
[10 11 20]
Example 3: min() With keepdims
When keepdims = True
, the dimensions of the resulting array matches the dimension of an input array.
import numpy as np
array1 = np.array([[10, 17, 25],
[15, 11, 22]])
print('Dimensions of original array: ', array1.ndim)
minValue = np.min(array1, axis = 1)
print('\n Without keepdims: \n', minValue)
print('Dimensions of array: ', minValue.ndim)
# set keepdims to True to retain the dimension of the input array
minValue = np.min(array1, axis = 1, keepdims = True)
print('\n With keepdims: \n', minValue)
print('Dimensions of array: ', minValue.ndim)
Output
Dimensions of original array: 2 Without keepdims: [10 11] Dimensions of array: 1 With keepdims: [[10] [11]] Dimensions of array: 2
Without keepdims
, the result is simply a one-dimensional array of smallest numbers.
With keepdims
, the resulting array has the same number of dimensions as the input array.
Example 4: min() With initial
We use initial
to define the maximum value np.min()
can return. If the minimum value of the array is larger than the initial value, initial
is returned.
import numpy as np
# min value < initial, returns min value
array1 = np.array([[10, 25, 17, 16, 14]])
minValue = np.min(array1, initial = 16)
print(minValue)
# min value > initial, returns initial
array2 = np.array([[10, 25, 17, 16, 14]])
minValue = np.min(array2, initial = 6)
print(minValue)
# in case of an empty array, initial value is returned
array3 = np.array([])
minValue = np.min(array3, initial = 5)
print(minValue)
Output
10 6 5.0
Example 5: min() With where Argument
The optional argument where
specifies elements to include in the calculation of minimum value.
import numpy as np
arr = np.array([[12, 25, 32],
[47, 50, 36]])
# take min of entire array
result1 = np.min(arr)
# min of only odd elements
result2 = np.min(arr, initial = 50, where = (arr%2==1))
# min of numbers greater than 30
result3 = np.min(arr, initial = 50,where = (arr > 30))
print('min of entire array:', result1)
print('min of only odd elements:', result2)
print('min of numbers greater than 30:', result3)
Output
min of entire array: 12 min of only odd elements: 25 min of numbers greater than 30: 32